FPGA implementation of principal component regression (PCR) for real-time differentiation of dopamine from interferents.

Abstract

This paper reports on field-programmable gate array (FPGA) implementation of a digital signal processing (DSP) unit for real-time processing of neurochemical data obtained by fast-scan cyclic voltammetry (FSCV) at a carbonfiber microelectrode (CFM). The DSP unit comprises a decimation filter and two embedded processors to process the FSCV data obtained by an oversampling recording front-end and differentiate the target analyte from interferents in real time with a chemometrics algorithm using principal component regression (PCR). Interfaced with an integrated, FSCV-sensing front-end, the DSP unit successfully resolves the dopamine response from that of pH change and background-current drift, two common dopamine interferents, in flow injection analysis involving bolus injection of mixed solutions, as well as in biological tests involving electrically evoked, transient dopamine release in the forebrain of an anesthetized rat.